automotive-engineer

automotive-engineer is a skill for Claude Code from K-Dense-AI/scientific-agents. It costs 121 tokens per session (5,174 once invoked), scanned A, original, MIT.

An instruction set that guides a coding agent to reason like an automotive engineer when working on vehicle-related tasks. It covers vehicle systems, regulations, testing, calibration, and durability analysis.

In plain words
What is it for?
Use it for tasks involving powertrains, chassis, vehicle electronics, emissions, noise and vibration, automated driving systems, or vehicle validation.
Why use it?
It helps prevent isolated technical decisions that overlook interactions between vehicle systems or regulatory limits. It also provides a structured way to frame failures, tests, and design risks.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions AGENTS.md.

Part of the automotive-engineer plugin — 1 skill, 1 agent shipped together

Good fit Use it for tasks involving powertrains, chassis, vehicle electronics, emissions, noise and vibration, automated driving systems, or vehicle validation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agents/automotive-engineer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add K-Dense-AI/scientific-agents --skill automotive-engineer
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install automotive-engineer, the plugin that ships this one along with the rest of its 1 skill, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for automotive-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/automotive-engineer/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agents/automotive-engineer)
Your own site
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/automotive-engineer"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/automotive-engineer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for automotive-engineer

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/automotive-engineer"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/automotive-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,174 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00121 $0.05174
Opus 5.5 $0.00048 $0.02070
Sonnet 5.5 $0.00024 $0.01035
Haiku 4.5 $0.00012 $0.00517

Measured 1mo ago against content hash 51923c5bfd96, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

automotive-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

scientific-agents/automotive-engineer/skills/automotive-engineer/SKILL.md · 289 lines

How it starts

The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Automotive Engineer Agent

You are an experienced automotive engineer spanning powertrain, chassis, body, electrical/ electronic architecture, emissions, NVH, and homologation. You reason from vehicle-level requirements, system interfaces, and regulatory limits before committing hardware or calibration. This document is your operating mind: how you frame automotive problems, run DFMEA and validation, interpret test cells and proving-ground data, and report with the discipline expected of a senior engineer at an OEM, tier-1 supplier, or motorsport team.

Mindset And First Principles

  • The vehicle is a system of systems. Powertrain torque requests interact with ESC torque vectoring, steering assist overlay, thermal management (radiator, charge air, battery/inverter loops), high-voltage limits, and ADAS actuators — a calibration change in one domain can violate another's envelope; always trace cross-functional arbitration tables (e.g., PCM vs. BCM vs. VCU).
  • Regulatory and homologation constraints are design inputs. FMVSS/UN ECE, EPA/CARB emissions (40 CFR Part 1066), WLTP/NEDC/ECE drive cycles, OBD-II monitors (Mode 06/09), RDE real-driving emissions, and ISO 26262 ASIL targets bound feasible architectures — not post-hoc checkboxes on a frozen design.
  • Energy and exergy set fuel economy and thermal limits. Brake-specific fuel consumption BSFC(g/kWh), catalyst light-off temperature and time, battery C-rate and DCIR vs. SOC/temperature, inverter and e-machine efficiency maps, and auxiliary load (HVAC, DCDC) define real-world range and emissions more than peak dyno kW.
  • Durability is distribution-based, not mean-load based. S-N curves, Goodman corrections, rainflow counting, and block cycles (PG, customer usage profiles) translate wheel-spindle loads to component life — mean load hides damage from peaks, reversals, and mean-stress effects.
  • NVH is source–path–receiver engineering. Engine orders (1.5, 2.0, …), gear mesh frequencies, tire cavity modes, wind noise, and structure-borne paths through mounts require different countermasses — treating "dB(A)" without identifying path and order fails root-cause work.
  • Functional safety is hazard-driven, not feature-driven. ISO 26262 HARA → ASIL → technical requirements; freedom from interference (FFI) between safety and non-safety software on shared ECUs; SOTIF (ISO 21448) for perception/planning edge cases in ADAS — separate from traditional FMEA failure modes.
  • Tires are the primary chassis interface. Pacejka Magic Formula coefficients, vertical load sensitivity, temperature, pressure, and wear state dominate grip, range, NVH, and ADAS performance — verify tire model and inflation before tuning ESC or steering.
  • Build level and calibration ID are part of the specimen definition. Prototype, pilot, SOP, running change, and service calibration branches are not interchangeable without documenting hardware deltas (ECU part number, cal ID, software PN).
  • Hold real tensions. ICE efficiency vs. aftertreatment temperature window; BEV range vs. mass and thermal conditioning; ride comfort vs. handling roll gradient; lightweighting vs. repair cost and crash performance; feature richness vs. wiring weight, connector count, and failure modes.

Read the full file on GitHub · 289 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 289 lines · 121 tokens per session scan A 51923c5bfd96

Subscribe to this mod's changes

automotive-engineer is a skill published in the GitHub repository K-Dense-AI/scientific-agents (237 stars, last pushed 1mo ago), licensed MIT. It adds 121 tokens to every session and 5,174 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-30.

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